Distributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics. The book is divided into three sections: theory, algorithms, and applications. Part one covers the background of information theory with an emphasis on DSC; part two discusses designs of algorithmic solutions for DSC problems, covering the three most important DSC problems: Slepian-Wolf, Wyner-Ziv, and MT source coding; and part three is dedicated to a variety of potential DSC applications. Key features: <ul>
<li>Clear explanation of distributed source coding theory and algorithms including both lossless and lossy designs.</li> <li>Rich applications of distributed source coding, which covers multimedia communication and data security applications.</li> <li>Self-contained content for beginners from basic information theory to practical code implementation.</li> </ul>
The book provides fundamental knowledge for engineers and computer scientists to access the topic of distributed source coding. It is also suitable for senior undergraduate and first year graduate students in electrical engineering; computer engineering; signal processing; image/video processing; and information theory and communications.

Distributed Source Coding: Theory and Practice

Computers & Technology

WANG, SHUANG

Fang, Yong

Cheng, Samuel

Computer Science

wiley

Most of the existing portfolio selection models are based on the probability theory. Though they often deal with the uncertainty via probabilistic - proaches, we have to mention that the probabilistic approaches only partly capture the reality. Some other techniques have also been applied to handle the uncertainty of the ?nancial markets, for instance, the fuzzy set theory [Zadeh (1965)]. In reality, many events with fuzziness are characterized by probabilistic approaches, although they are not random events. The fuzzy set theory has been widely used to solve many practical problems, including ?nancial risk management. By using fuzzy mathematical approaches, quan- tative analysis, qualitative analysis, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model. The contents of this book mainly comprise of the authors’ research results for fuzzy portfolio selection problems in recent years. In addition, in the book, the authors will also introduce some other important progress in the ?eld of fuzzy portfolio optimization. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. A new framework for investment analysis is presented in this book. A series of portfolio sel- tion models are given and some of them might be more e?cient for practical applications. Some application examples are given to illustrate these models by using real data from the Chinese securities markets.

Fuzzy Portfolio Optimization: Theory and Methods (Lecture Notes in Economics and Mathematical Systems, 609)

Business & Money

Wang, Shouyang

Lai, Kin-Keung

Economics

Springer

孟子(精)/中华经典名著全本全注全译丛书

中华书局

藏山蕴海•北大建筑与园林(第2版)

Peking University Press

Zhuangzi quan ping: Quan ben Zhuangzi hui zhu hui ping (Mandarin Chinese Edition)

Reference

Foreign Language Study & Reference

Ba Shu shu she

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